All these methods require access to the group's labels at train time, condition which might not be met on certain use-cases. It is also interesting to note that existing group labels may alternatively not be informative of actual harmful biases. Different methods were then proposed to treat these more complex cases. They can be based on either proposing potential confounding variables [1]}, or on approaches from the field of Distributionally Robust Optimization (DRO) [2]}. In particular, this latter family of methods has been a focal point for the emerging field of sub-population shift, or group shift, whereby the training distribution can be subdivided into multiple groups (oftentimes without labels) and the test distribution becomes the one of the group on which the model performs the worst.
